312 lines
8.8 KiB
Python
312 lines
8.8 KiB
Python
# scipy-yli: Helpful SciPy utilities and recipes
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# Copyright © 2022 Lee Yingtong Li (RunasSudo)
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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import numpy as np
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import pandas as pd
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import patsy
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import warnings
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from .config import config
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# ----------------------------
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# Data cleaning and validation
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def check_nan(df, nan_policy):
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"""Check df against nan_policy and return cleaned input"""
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if nan_policy == 'raise':
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if pd.isna(df).any(axis=None):
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raise ValueError('NaN in input, pass nan_policy="warn" or "omit" to ignore')
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elif nan_policy == 'warn':
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df_cleaned = df.dropna()
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if len(df_cleaned) < len(df):
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warnings.warn('Omitting {} rows with NaN'.format(len(df) - len(df_cleaned)))
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return df_cleaned
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elif nan_policy == 'omit':
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return df.dropna()
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else:
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raise Exception('Invalid nan_policy, expected "raise", "warn" or "omit"')
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def as_2groups(df, data, group):
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"""Group the data by the given variable, ensuring only 2 groups"""
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# Get groupings
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groups = list(df.groupby(group).groups.items())
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# Ensure only 2 groups to compare
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if len(groups) != 2:
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raise Exception('Got {} values for {}, expected 2'.format(len(groups), group))
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# Get 2 groups
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group1 = groups[0][0]
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data1 = df.loc[groups[0][1], data]
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group2 = groups[1][0]
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data2 = df.loc[groups[1][1], data]
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return group1, data1, group2, data2
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# ----------
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# Formatting
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def do_fmt_p(p):
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"""Return sign and formatted p value"""
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if p < 10**-config.pvalue_max_dps:
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# Smaller than min value
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return '<', '{:.1g}'.format(10**-config.pvalue_max_dps)
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if p > 1 - 10**-config.pvalue_min_dps:
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# Larger than max value
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return '>', '{0:.{dps}f}'.format(1 - 10**-config.pvalue_min_dps, dps=config.pvalue_min_dps)
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if round(p, config.pvalue_min_dps) == config.alpha:
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# Rounding to pvalue_min_dps makes significance ambiguous
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if round(p, config.pvalue_max_dps) == config.alpha:
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# Still ambiguous to pvalue_max_dps
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if p < config.alpha:
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# Significant: round down
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p = config.alpha - 10**-config.pvalue_max_dps
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else:
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# Nonsignificant: round up
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p = config.alpha + 10**-config.pvalue_max_dps
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return None, '{0:.{dps}f}'.format(p, dps=config.pvalue_max_dps)
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if p < 10**-config.pvalue_min_dps:
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# Insufficient resolution at pvalue_min_dps
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# We know from earlier comparison that 1 s.f. fits within pvalue_max_dps
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return None, '{:.1g}'.format(p)
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# OK to round to pvalue_min_dps
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return None, '{0:.{dps}f}'.format(p, dps=config.pvalue_min_dps)
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def fmt_p(p, *, html, tabular=False):
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"""
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Format p value
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tabular: If true, output in ‘tabular’ format of p values where decimal points align
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"""
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sign, fmt = do_fmt_p(p)
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# Strip leading zero if required
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if not config.pvalue_leading_zero:
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fmt = fmt.lstrip('0')
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# Add significance asterisk if required
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if p < config.alpha:
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fmt += '*'
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if sign is not None:
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if html:
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# Escape angle quotes
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sign = sign.replace('<', '<')
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sign = sign.replace('>', '>')
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if tabular:
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pfmt = sign + fmt # e.g. "<0.001"
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else:
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pfmt = sign + ' ' + fmt # e.g. "< 0.001"
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else:
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if tabular:
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# Tabular format with no sign: add a preceding space to get decimal points to line up
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if html:
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# Insert space with width of '=' which should be width of '<' and '>' too
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pfmt = '<span style="visibility:hidden">=</span>' + fmt # e.g. "0.05"
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else:
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pfmt = ' ' + fmt # e.g. "0.05"
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else:
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pfmt = '= ' + fmt # e.g. "= 0.05"
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return pfmt
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# ------------------------------
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# General result-related classes
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class ConfidenceInterval:
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"""A confidence interval"""
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def __init__(self, lower, upper):
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#: Lower confidence limit (*float*)
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self.lower = lower
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#: Upper confidence limit (*float*)
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self.upper = upper
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def _repr_html_(self):
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return self.summary()
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def summary(self):
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"""
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Return a stringified summary of the confidence interval
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:rtype: str
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"""
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return '{:.2f}–{:.2f}'.format(self.lower, self.upper)
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class Estimate:
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"""A point estimate and surrounding confidence interval"""
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def __init__(self, point, ci_lower, ci_upper):
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#: Point estimate (*float*)
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self.point = point
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#: Lower confidence limit (*float*)
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self.ci_lower = ci_lower
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#: Upper confidence limit (*float*)
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self.ci_upper = ci_upper
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def _repr_html_(self):
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return self.summary()
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def summary(self):
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"""
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Return a stringified summary of the estimate and confidence interval
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:rtype: str
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"""
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return '{:.2f} ({:.2f}–{:.2f})'.format(self.point, self.ci_lower, self.ci_upper)
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def __neg__(self):
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return Estimate(-self.point, -self.ci_upper, -self.ci_lower)
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def __abs__(self):
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if self.point < 0:
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return -self
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else:
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return self
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def exp(self):
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return Estimate(np.exp(self.point), np.exp(self.ci_lower), np.exp(self.ci_upper))
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# --------------------------
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# Patsy formula manipulation
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def cols_for_formula(formula, df):
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"""Return the columns corresponding to the Patsy formula"""
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# Parse the formula
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model_desc = patsy.ModelDesc.from_formula(formula)
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# Get the columns
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cols = set()
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for term in model_desc.rhs_termlist:
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for factor in term.factors:
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name = factor.name()
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if name.startswith('C('):
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# Contrasts expression
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# Get the corresponding factor_info
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factor_info = formula_get_factor_info(formula, df, name)
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# Evaluate the factor
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categorical_box = factor_info.factor.eval(factor_info.state, df)
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# Get the column name
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name = categorical_box.data.name
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cols.add(name)
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return list(cols)
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def formula_get_factor_info(formula, df, factor):
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"""Get the FactorInfo for a factor in a Patsy formula"""
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# Parse the formula
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design_info = patsy.dmatrix(formula, df).design_info
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# Get the corresponding factor_info
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factor_info = next(v for k, v in design_info.factor_infos.items() if k.name() == factor)
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return factor_info
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def formula_factor_ref_category(formula, df, factor):
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"""Get the reference category for a term in a Patsy formula referring to a categorical factor"""
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if '(' in factor and not factor.startswith('C('):
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raise Exception('Attempted to get reference category for unknown expression type "{}"'.format(factor))
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# Get the factor_info
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factor_info = formula_get_factor_info(formula, df, factor)
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if '(' not in factor:
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# C(...) is not specified, so must be default
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return factor_info.categories[0]
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# Evaluate the factor
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categorical_box = factor_info.factor.eval(factor_info.state, df)
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if categorical_box.contrast is None or categorical_box.contrast is patsy.Treatment:
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# Default Treatment contrast with default reference group: first category
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return factor_info.categories[0]
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if isinstance(categorical_box.contrast, patsy.Treatment):
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if categorical_box.contrast.reference is None:
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# Default reference group: first category
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return factor_info.categories[0]
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# Specified reference group
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return categorical_box.contrast.reference
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raise Exception('Attempted to get reference category for unknown contrast type {}'.format(categorical_box.contrast.__class__.__name__))
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def parse_patsy_term(formula, df, term):
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"""
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Parse a Patsy term into its component parts
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Returns: factor, column, contrast
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e.g. "C(x, Treatment(y))[T.z]" -> "C(x, Treatment(y))", "x", "z"
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"""
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if '(' not in term:
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if '[' in term:
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if '[T.' not in term:
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raise Exception('Attempted to parse term for unknown contrast type "{}"'.format(term))
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# Treatment contrast term
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factor = term[:term.index('[T.')]
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contrast = term[term.index('[T.')+3:term.index(']')]
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return factor, factor, contrast
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else:
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# Nothing special
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return term, term, None
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# Term contains '('
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if not term.startswith('C('):
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raise Exception('Attempted to parse term for unknown expression type "{}"'.format(term))
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if '[' in term:
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if '[T.' not in term:
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raise Exception('Attempted to parse term for unknown contrast type "{}"'.format(term))
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# Treatment contrast term
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factor = term[:term.index('[T.')]
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contrast = term[term.index('[T.')+3:term.index(']')]
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else:
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# Not a treatment contrast (I think this is impossible?)
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raise Exception('Attempted to parse unsupported contrast-like term with no contrasts')
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factor_inner = factor[factor.index('(')+1:factor.rindex(')')]
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if ',' in factor_inner:
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column = factor_inner[:factor_inner.index(',')]
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else:
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column = factor_inner
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return factor, column, contrast
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